Systems and methods for generating soft information in a flash device

ABSTRACT

Systems and methods are provided for generating a soft information metric corresponding to a bit stored in a memory. The systems and methods include comparing a symbol value associated with the stored bit to a plurality of decision thresholds to obtain a plurality of binary values. One of the plurality of binary values is selected to obtain a reference value. Further, a frequency metric is computed, which corresponds to the number of times each of the plurality of binary values equals a predefined value. The soft information metric is then determined based on the frequency metric and the reference value.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/285,349, filed on May 22, 2014 (allowed), which claims the benefitunder 35 U.S.C. § 119(e) of U.S. Provisional Application No. 61/827,154,filed on May 24, 2013, both of which are incorporated herein byreference in their respective entireties.

FIELD OF USE

The present disclosure relates generally to systems and methods forgenerating soft information in storage systems, such as storage systemsusing flash memory. The soft information may be used by a decoder toreduce the number of read errors.

BACKGROUND OF THE DISCLOSURE

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of theinventors hereof, to the extent the work is described in this backgroundsection, as well as aspects of the description that may not otherwisequalify as prior art at the time of filing, are neither expressly norimpliedly admitted as prior art against the present disclosure.

The disclosed technology relates to data storage systems, and moreparticularly, to generating soft information for the decoding of data inflash memories.

In a data storage system, it is desirable for information, often groupedin blocks or sectors, to be accurately stored and retrieved. A decoder,either integrated within or external to the data storage system,processes a signal retrieved from the data storage device. The medium,or media, on which the data is stored may corrupt the retrieved signalsuch that the decoder is unable to correctly reconstruct the storedinformation. Accordingly, given a storage medium, sufficient reliabilityis obtained through careful design of the data storage system and/or thedecoder, and of their respective components.

The input to a decoder may include different types of information aboutthe symbols that are to be decoded. For example, the information mayinclude hard information, soft information, or a combination of both.Hard information generally corresponds to a single symbol value,selected from a set of admissible values, any one of which could beassociated with the symbol. For example, if symbols are associated withbinary values, the set of admissible values may correspond to logicalzero (“0”) and logical one (“1”). In this example a signal containinghard information would associate each symbol contained in the signalwith either a logical zero or a logical one.

In converting the retrieved signal of the storage device to hardinformation, some information is lost, because the stored informationmust be associated with one of the predefined admissible values. Forexample, when processing binary information, stored symbols must beassociated with either a logical zero or a logical one. Therefore, basedon hard information alone, it is not possible to determine thelikelihood that a given symbol of the retrieved signal indeedcorresponds to the admissible value assigned to that symbol. Forexample, when processing binary information, hard information does notconvey whether a given symbol of the retrieved signal was a “strong” ora “weak” logical zero or logical one. In other words, information aboutthe uncertainty introduced by associating a given symbol with a logicalzero or a logical one is not part of the hard information.

Soft information, in contrast to hard information, generally includes alikelihood metric that conveys the likelihood that a given one of thepredefined admissible values gave rise to the stored symbol. Likelihoodvalues for several or all of the admissible values may be included. Forexample, assuming a binary coding scheme, soft information may includetwo likelihood metrics: one that provides the likelihood that the givensymbol corresponds to a logical zero and one that provides thelikelihood that the given symbol corresponds to a logical one. For thebinary case, one of these likelihoods may be omitted because it can bederived from the other. In contrast to hard information, softinformation is able to capture whether a given symbol is a “weak” or a“strong” logical zero or logical one. Accordingly, the decoder isprovided with additional information that may be used to improvedecoding performance.

In spite of the potential to improve decoding performance, softinformation may lead to additional complexity in practicalimplementations. In practical implementations, the likelihood metricsassociated with each of the predefined admissible values may need to bequantized to a finite number of quantization levels. Representing softinformation may therefore be significantly more complex compared to hardinformation. It is therefore important to find efficient ways ofrepresenting soft information such that the benefits of improveddecoding performance outweigh the additional complexity in practicalimplementations.

SUMMARY OF THE DISCLOSURE

In accordance with an embodiment of the present disclosure, a method isprovided for generating a soft information metric corresponding to a bitstored in a memory. The method may include comparing a symbol valueassociated with the stored bit to a plurality of decision thresholds toobtain a plurality of binary values. Further, one of the plurality ofbinary values may be selected to obtain a reference value, and afrequency metric may be computed, which corresponds to the number oftimes each of the plurality of binary values equals a predefined value.The soft information metric may then be determined based on thefrequency metric and the reference value.

In some implementations, the stored bit may be part of a sequence ofstored bits, and the method may further include determining a decisionthreshold of the plurality of decision thresholds based on a position ofthe stored bit in the sequence of stored bits.

In some implementations, the soft information metric is a log-likelihoodratio (LLR) metric.

In some implementations, determining the soft information metric mayfurther include determining an index value based on the frequency metricand the reference value and identifying the soft information metric in alookup table based on the index value.

In some implementations, the number of decision thresholds in theplurality of decision thresholds may be determined based on a desiredquantization level associated with the soft information metric.

In some implementations, the number of decision thresholds in theplurality of decision thresholds may be increased in response todetermining that a previous decoding attempt has failed.

In some implementations, the method may further include generating anindex value based on the frequency metric and the reference value andstoring a histogram of at least one of the index value, the frequencymetric, and the reference value to obtain statistics of the softinformation metric.

In accordance with another embodiment of the present disclosure, asystem is provided for generating a soft information metriccorresponding to a bit stored in a memory. The system may include ademodulator configured to compare a symbol value associated with thestored bit to a plurality of decision thresholds to obtain a pluralityof binary values. The system may further include control circuitryconfigured to select one of the plurality of binary values to obtain areference value, compute a frequency metric corresponding to a number oftimes each of the plurality of binary values equals a predefined value,and determine the soft information metric based on the frequency metricand the reference value.

In some implementations, the stored bit may be part of a sequence ofstored bits and the demodulator may be further configured to determine adecision threshold of the plurality of decision thresholds based on aposition of the stored bit in the sequence of stored bits.

In some implementations, the soft information metric may be alog-likelihood ratio (LLR) metric.

In some implementations, the control circuitry may be further configuredto determine an index value based on the frequency metric and thereference value and identify the soft information metric in a lookuptable based on the index value.

In some implementations, the demodulator may be further configured todetermine the number of decision thresholds in the plurality of decisionthresholds based on a desired quantization level associated with thesoft information metric.

In some implementations, the demodulator may be further configured toincrease the number of decision thresholds in the plurality of decisionthresholds in response to determining that a previous decoding attempthas failed.

In some implementations, the control circuitry may be further configuredto generate an index value based on the frequency metric and thereference value and store a histogram of at least one of the indexvalue, the frequency metric, and the reference value to obtainstatistics of the soft information metric.

In another embodiment of the present disclosure, a system is providedfor generating a soft information metric. The system may include abuffer register configured to buffer a plurality of bit values retrievedfrom a data storage device, a comparator configured to compare theplurality of bit values buffered in the buffer register to a referencevalue to obtain a plurality of comparison values, and an accumulationregister that stores a frequency metric, wherein the frequency metric isincremented based on the plurality of comparison values.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features of the disclosure, its nature, and various advantages,will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 shows an illustrative diagram of a data storage system thatutilizes soft information to improve decoding reliability, in accordancewith some embodiments of the present disclosure;

FIG. 2 shows illustrative diagrams of decision regions associated withstorage systems that use 1-bit per cell and 2-bit per cell modulationschemes, in accordance with some embodiments of the present disclosure;

FIG. 3 shows illustrative mapping tables for encoding decision regionsbased on a frequency metric and a reference value, in accordance withsome embodiments of the present disclosure;

FIG. 4 shows a block diagram of hardware architecture 400 for generatingsoft information in a data storage system, in accordance with someembodiments of the present disclosure;

FIG. 5 shows a block diagram of soft information metric determinationcircuitry, in accordance with some embodiments of the presentdisclosure;

FIG. 6 shows an illustrative example of lookup table circuitry, inaccordance with some embodiments of the present disclosure;

FIG. 7 shows an illustrative timing diagram of decoding hardwareoperating in a non-retry mode, in accordance with an embodiment of thepresent disclosure;

FIG. 8 shows an illustrative timing diagram for using decoding hardwareoperating in a retry mode, in accordance with some embodiments of thepresent disclosure;

FIG. 9 shows a high-level flow chart for determining soft information ina data storage system, in accordance with some embodiments of thepresent disclosure; and

FIG. 10 shows a block diagram of a computing device, for performing anyof the processes described herein, in accordance with some embodimentsof the present disclosure.

DETAILED DESCRIPTION

The present disclosure generally relates to a hardware architecture forgenerating soft information in a data storage system. In one aspect, thesoft information may be encoded efficiently by utilizing a frequencymetric and a reference value.

FIG. 1 shows an illustrative block diagram of a data storage system 100that utilizes soft information to improve decoding reliability, inaccordance with some embodiments of the present disclosure. Blockdiagram 100 includes flash device 102, soft information generator 110,and decoder 112. Soft information generator 110 may be connected toflash device 102 through NAND interface 106 and buffer 108. Softinformation generator 110 and NAND interface 106 may be connected tocentral processing unit (CPU) 104, which may coordinate their respectiveoperation.

NAND interface 106 may coordinate write operations to and readoperations from flash device 102. In some implementations, NANDinterface 106 may read from flash device 102 by applying a certain readreference voltage and processing the resulting signal retrieved fromflash device 102. Multiple read operations (for example, 15 readoperations) may be performed with respect to the same stored symbol, andeach read operation may be associated with a different read referencevoltage. A symbol may refer to a physical signal, stored or transmitted,that represents an integer number of bits. For example, in someembodiments of the present disclosure, a symbol may refer to the amountof charge that is stored in a cell (e.g., in a transistor) of flashdevice 102. Data signal 107, the output signal of NAND interface 106,may include binary values (i.e., either logical zeroes or logical ones)associated with each of the one or more read operations performed byNAND interface 106. The binary information may represent hardinformation associated with each of the read operations. However,because multiple versions of hard information are available, each ofwhich corresponds to a different value of the read reference voltage,soft information may be generated based on the retrieved signals.

Buffer 108 takes data signal 107 as input and buffers the retrieved dataassociated with different read operations. In some implementations, thebuffering of data signal 107 may facilitate the generation of softinformation by soft information generator 110. In other implementations,such buffering may not be necessary and consequently buffer 108 may beomitted without departing from the scope of the present disclosure.

Soft information generator 110 may be connected to buffer 108 and maytake buffered data signal 109 as input. For each symbol stored in flashdevice 102, buffered data signal 109 may include a predefined number ofbinary values, each of which corresponds to a separate read operationwith a different read reference voltage. For example, 15 binary valuesmay be retrieved by NAND interface 106 for each symbol stored in flashdevice 102, and each of the 15 binary values may be associated with adifferent read reference voltage. Of course, a smaller or larger numberof binary values may be used without departing from the scope of thepresent disclosure. Soft information generator 110 processes buffereddata signal 109 to obtain a more compact representation of the retrieveddata. For example, in some embodiments, soft information generator 110may generate a frequency metric and a reference value to represent thebinary data associated with each symbol. In some implementations, thefrequency metric may denote the number of logical zeroes or logical onesthat occur in the binary data of a given symbol. The reference value maycorrespond to the binary value associated with a specific one of theread reference voltage levels. For example, if a sequence of 15 binaryvalues is associated with a given symbol, the binary value associatedwith the last entry in the sequence may be used as the reference value.

Any combination of the frequency metric and the reference value may beassociated with a specific soft information metric. For example, if thefrequency metric indicates that a majority of entries in the sequenceare associated with a logical zero, this may serve as an indication thatthere is a high likelihood that the retrieved symbol indeed correspondsto a logical zero. On the other hand, if the frequency metric indicatesthat a similar number of logical zeroes and logical ones is present,this may serve as an indication that the likelihood of the retrievedsymbol having been caused by a logical zero or a logical one isapproximately equal. In some embodiments, soft information may begenerated based on both the frequency value and the reference value. Theabove example serves only as an illustration of how soft information maybe generated. The generation of soft information based on a frequencymetric and a reference value may be performed in other ways, withoutdeparting from the scope of the present disclosure, as is discussed inthe following.

The frequency metric and the reference value may be used together togenerate a soft information metric. In some implementations, thefrequency metric and the reference value may represent indices that areused to look up a specific soft information value in a lookup table.However, other representations of buffered data signal 109, or buffereddata signal 109 itself, may be used to obtain the soft information. Theoutput of soft information generator 110 may be soft information signal111. CPU 104 may control the generation of soft information signal 111by providing the lookup tables used as part of the generation process.CPU 104 may also control the granularity of the soft information to begenerated. For example, in normal operation, soft information may bequantized coarsely or omitted entirely. However, if decoder 112 fails todecode the data based on the coarse information, additional data may beretrieved and represented as soft information with a higher degree ofaccuracy.

Decoder 112 may perform decoding of the data signal retrieved from flashmemory 102 based on soft information signal 111. In someimplementations, decoder 112 may be a low density parity check (LDPC)decoder, although other decoder types may also be used. Decoder 112 mayrepresent the soft information used for decoding in different ways, suchas by associating a log-likelihood ratio (LLR) with each member of theset of admissible symbol values.

FIG. 2 shows illustrative diagrams of decision regions associated withstorage systems that use 1-bit per cell and 2-bit per cell modulationschemes, in accordance with some embodiments of the present disclosure.Diagram 200 may correspond to a 1-bit per cell storage system in whicheach symbol stored on flash device 102 encodes a single bit.Accordingly, in the absence of corruption of the data during storage orretrieval, the stored symbols may correspond to any one of two values.These two admissible values are represented by signal constellationpoints 202 a and 202 b (generally signal constellation points 202). Insome implementations, pulse amplitude modulation (PAM) may be used asthe modulation scheme, which encodes information in the amplitude of thesignal. For example, signal constellation point 202 a may correspond toa first amplitude value and signal constellation point 202 b maycorrespond to a second amplitude value. When corruption of the signal ispresent, the actual charge level or symbol value may deviate from signalconstellation points 202. In many systems, such corruption may beapproximated by normal distributions 204 a and 204 b (generally normaldistribution 204) with zero-mean and a certain variance. The corruptionto the signal is generally additive. Therefore, if a certain storedsymbol corresponds to signal constellation point 202 a, the symbol valueafter corruption has been taken into account may be given by normaldistribution 204 a. Normal distribution 204 a may denote the probabilitydensity function under the hypothesis that the stored bit is associatedwith signal constellation point 202 a and it is therefore centered atthe amplitude value corresponding to signal constellation point 202 a(in accordance with the assumption that the corruption has zero-mean).Similarly, if the stored symbol is associated with signal constellationpoint 202 b, the corruption would lead to conditional probabilitydensity function 204 b, which is centered at the amplitude valuecorresponding to signal constellation point 202 b.

The symbol values subject to corruption may need to be converted to hardinformation by NAND interface 106 prior to being used by decoder 112.The conversion may be performed by comparing the corrupted symbol valueto a decision threshold, such as decision threshold 206 b. If theretrieved symbol value lies above (or to the right of) decisionthreshold 206 b, then a logical zero may be recorded (assuming thatsignal constellation point 202 b corresponds to a logical zero) and ifthe retrieved symbol value lies below (or to the left of) decisionthreshold 206 b then a logical one may be recorded (assuming that signalconstellation point 202 a denotes a logical one). Decision threshold 206b may be selected such that it lies at the center between signalconstellation points 202 a and 202 b. At this center point theconditional probability density functions 204 a and 204 b intersect or,in other words, if a symbol value above center point 203 is observed, itis more likely that this symbol corresponds to signal constellationpoint 202 b and vice versa.

As is discussed in relation to FIG. 1, the location of decisionthreshold 206 b may be adjusted by applying a different read referencevoltage, and multiple instances of hard information may be obtained fordifferent decision thresholds. For example, decision thresholds 206 aand 206 c may be used in addition to decision threshold 206 b. Thesymbol value may be compared separately with decision thresholds 206 a,206 b, and 206 c (generally decision thresholds 206) to obtain asequence of three binary values. Together these three binary values mayindicate one of four decision regions in which the symbol value mustlie. In particular, these decision regions may be given by: (1) firstdecision region 208 a corresponds to symbol values below threshold 206a; (2) second decision region 208 b corresponds to symbol values betweendecision threshold 206 a and 206 b; (3) third decision region 208 ccorresponds to symbol values between decision threshold 206 b and 206 c;and (4) fourth decision region 208 d corresponds to symbol values abovedecision threshold 206 c. The sequence of binary values, or equivalentlytheir associated decision regions, may be converted to a softinformation metric because they provide a likelihood that the symbolvalue corresponds to a logical zero or a logical one. For example, if asymbol value lies in first decision region 208 a, it is more likely thatthis symbol value is associated with a logical one than if the symbolvalue were associated with second decision region 208 b, although bothfirst and second decision regions would be mapped to a logical one if ahard decision based on decision threshold 206 b were made. In otherwords, if a symbol value falls into first decision region 208 a, it maybe viewed as a “strong” logical one, whereas if it falls into seconddecision region 208 b, it may be viewed as a “weak” logical one.Similarly, if a symbol value falls into third decision region 208 c, itmay be viewed as a weak logical zero, and if it falls into fourthdecision region 208 d it may be viewed as a strong logical zero.

Diagram 200 illustrates the generation of soft information based onmultiple decision thresholds for a modulation scheme that associateseach data symbol with a single bit. For example, this may berepresentative of a flash memory that stores one bit per memory cell.However, multiple decision thresholds may also be used with modulationschemes that associate multiple bits with each data symbol. For example,this may be representative of a flash memory that stores two, three, ormore bits per memory cell. Diagram 250 illustrates a modulation schemein which two bits are stored per memory cell. Diagram 250 encodes thetwo bits based on four signal constellation points 252 a, 252 b, 252 c,and 252 d (generally, signal constellation points 252). For example,signal constellation point 252 a may represent bit combination “11”(i.e., a sequence of two logical ones), signal constellation point 252 bmay represent bit combination “10,” signal constellation point 252 c mayrepresent bit combination “00,” and signal constellation point 252 d mayrepresent bit combination “01.” In some implementations, the first ofthe pair of bits may be referred to as the most significant bit (MSB)and the second bit may be referred to as the least significant bit(LSB).

When decoding the LSB, decision thresholds 256 a-f (generally decisionthresholds 256) may be placed around the center point of signalconstellation points 252 a and 252 b and the center point of signalconstellation points 252 c and 252 d, respectively. Placing decisionthresholds at the center point between signal constellation points 252 band 252 c may not be required because the LSB assigned to the bitsequences associated with signal constellation points 252 b and 252 c isboth equal to logical zero. Similar to diagram 200, decision thresholds256 partition diagram 250 into a number of decision regions. Inparticular, diagram 250 is associated with six decision regions: (1)first decision region 258 a is associated with symbol values belowdecision threshold 256 a; (2) second decision region 258 b is associatedwith symbol values between decision thresholds 256 a and 256 b; (3)third decision region 258 c is associated with symbol values betweendecision thresholds 256 b and 256 c; (4) fourth decision region 258 d isassociated with symbol values between decision thresholds 256 c and 256d; (5) fifth decision region 258 e is associated with symbol valuesbetween decision thresholds 256 d and 256 e; (6) sixth decision region258 f is associated with symbol values between decision thresholds 256 eand 256 f; and (7) seventh decision region 258 g is associated withsymbols values above decision threshold 256 f. Similar to diagram 200,each of the decision regions may be associated with a degree oflikelihood that the LSB is either a logical zero or a logical one. Forexample, first decision region 258 a and seventh decision region 258 gmay be associated with a strong logical one; second decision region 258b and sixth decision region 258 f may be associated with a weak logicalone; third decision region 258 c and fifth decision region 258 e may beassociated with a weak logical zero and the fourth decision region 258 dmay be associated with a strong logical zero.

FIG. 3 shows illustrative mapping tables for encoding decision regionsbased on a frequency metric and a reference value, in accordance withsome embodiments of the present disclosure. Table 300 shows an exampleencoding scheme for the decision regions shown in diagram 200, asdiscussed in relation to FIG. 2. Decision region 302 a may correspond tofirst decision region 208 a, decision region 302 b may correspond tosecond decision region 208 b, decision region 302 c may correspond tothird decision region 208 c, and decision region 302 d may correspond tofourth decision region 208 d. Each of decision regions 302 a-302 d(generally decision regions 302) may be associated with a sequence ofbits that is represented by a column in table 300. The entries of eachsequence of bits correspond to the binary value that is associated withone of the decision thresholds. For example, for region 302 a, the firstentry in the sequence is associated with decision threshold 206 a, thesecond entry in the sequence is associated with decision threshold 206b, and the third entry is associated with decision threshold 206 c. Theentry corresponding to a decision threshold indicates whether thedecision region lies to the left or to the right of the decisionthreshold, i.e., whether symbol values in that decision region aresmaller or larger than the decision threshold. For example, withreference to the first column of table 300, the first entry in thesequence (corresponding to threshold 304 a) is equal to logical one,because decision region 302 a is to the left of decision threshold 206a. Similarly, the second entry of the sequence (corresponding tothreshold 304 b) is logical one because decision region 302 a also liesto the left of decision threshold 206 b. Finally, the third entry of thesequence (corresponding to threshold 304 c) is equal to logical onebecause decision region 302 a is to the left of decision threshold 206c.

For a specific symbol value, soft information may be generated bydetermining in which of the decision regions the symbol value falls. Forexample, with reference to diagram 200, if a specific symbol value islocated between thresholds 206 a and 206 b, it would fall into seconddecision region 208 b. Accordingly, it would be assigned the bitsequence associated with second decision region 302 b in table 300.

In some embodiments, the sequence of bits may be used directly bydecoder 112 or it may be used by soft information generator 110 togenerate soft information. In some implementations, the sequence of bitsmay be compressed and represented by a frequency metric and a referencevalue, respectively. In particular, the bit sequences associated withdecision regions 302 may have the property that, unless all of theirentries are either logical zero or logical one, there is only a singletransition point between a sequence of logical ones and a sequence oflogical zeroes. Accordingly, to reconstruct the sequence, it may sufficeto know the number of logical zeroes (or equivalently the number oflogical ones) as well as the location of a reference value, such as thebinary value associated with the rightmost decision threshold. For theexample shown in table 300, frequency metric 306 may be zero for thefirst decision region (no zero entries are part of the sequence), onefor the second decisions region (a single zero is present), two for thethird decision region (two zero entries are present), and three for thefourth decision region (all three entries of the sequence are zero).Reference value 308 may be equal to the last sequence entry of eachdecision sequence. For the specific example in table 300, the referencevalue may not be needed to reconstruct the bit sequence of each decisionthreshold, but it may be required in other implementations as is shownbelow.

Table 350 shows an example encoding scheme for the decision regionsshown in diagram 250, as is discussed in relation to FIG. 2. Decisionregion 352 a may correspond to first decision region 258 a, decisionregion 352 b may correspond to second decision region 258 b, decisionregion 352 c may correspond to third decision region 258 c, decisionregion 352 d may correspond to fourth decision region 258 d, decisionregion 352 e may correspond to fifth decision region 258 e, decisionregion 352 f may correspond to sixth decision region 258 f, and decisionregion 352 g may correspond to seventh decision region 258 g. Each ofdecision regions 352 a-352 g (generally decision regions 352) may beassociated with a sequence of bits that is represented by a column intable 350. The entries of each sequence of bits correspond to the binaryvalue that is associated with one of the decision thresholds. In diagram350, decision thresholds 258 may be partitioned into three groups. Forexample, decision threshold 256 a may be grouped with decision threshold256 d, decision threshold 256 b may be grouped with decision threshold256 e, and decision threshold 256 c may be grouped with decisionthreshold 256 f. In table 350, row 354 b may refer to the first group ofdecision thresholds, row 354 a may refer to the second group of decisionthresholds, and row 354 c may refer to the third group of decisionthresholds. Each row in table 350 (i.e., each entry in the bit sequencesbelong to a particular decision region) may indicate whether thedecision region is located within the interval defined by the twodecision thresholds belonging to a group of decision thresholds. Forexample, row 354 b denotes whether the decision region lies outside ofthe interval defined by decision thresholds 256 b and 256 e (in whichcase a logical one is assigned in row 354 b), or if the decision regionlies within the interval defined by decision thresholds 256 b and 256 e(in which case a logical zero is assigned in row 354 b). Similarly, row354 a denotes whether the decision region lies outside of the intervaldefined by decision thresholds 256 a and 256 d (in which case a logicalone is assigned), or if the decision region lies within the intervaldefined by decision thresholds 256 a and 256 d (in which case a logicalzero is assigned). Similar to table 300, the bit sequences defined intable 350 may be used directly by decoder 112, or they may be used bysoft information generator 110 to generate soft information, such as anLLR. In some implementations, the bit sequences may be compressed andrepresented by a frequency metric and a reference value, respectively.The frequency metric may correspond to the number of logical zeroes (oralternatively, the number of logical ones) that are contained in the bitsequence. The reference value may correspond to the bit valuecorresponding to a predefined one of the decision thresholds 354.

FIG. 4 shows a block diagram of hardware architecture 400 for generatingsoft information in a data storage system, in accordance with someembodiments of the present disclosure. Hardware architecture 400 mayinclude soft information metric determination circuitry 402, lookuptable circuitry 404, and soft information registers 406. Softinformation metric determination circuitry 402 may receive the bitsequences of diagram 300 and 350 as input. As is discussed in relationto FIG. 2 and FIG. 3, each bit sequence may be associated with the valueof a retrieved data symbol and may reflect the location of the datasymbol with respect to several decision thresholds. Soft informationmetric determination circuitry 402 may determine a more compactrepresentation of bit sequence 408, for example by representing bitsequence 408 based on a frequency metric and a reference value. Thedetermination of the frequency metric may be performed by counting thenumber of logical zeroes contained in bit sequence 408. The referencevalue may be obtained by using the binary value associated with apredefined entry of the sequence as a reference value.

Lookup table circuitry 404 may receive the frequency metric and thereference value as input. Together the frequency metric and thereference value may be used to uniquely identify a soft informationmetric in a lookup table. For example, frequency metric and referencevalue may be converted to a single index that is in turn used toidentify a specific entry in a lookup table. The soft information metricstored in the lookup table may then be output as soft information signal410 and provided to decoder 112.

Soft information registers 406 may include programmable registers toprogram or modify lookup tables accessed by lookup table circuitry 404(not shown). Soft information registers 406 may also containinstructions on a desired quantization accuracy. For example, forinitial processing of a symbol, low-accuracy soft information (orpossibly even hard information) may be used, because it is faster orless computationally demanding. If decoder 112 is able to successfullyperform decoding based on the low-accuracy information, then furtherprocessing may not be required. However, if decoding based on thelow-accuracy information fails, then information with a higher degree ofaccuracy may be computed.

FIG. 5 shows a block diagram of soft information metric determinationcircuitry 500, in accordance with some embodiments of the presentdisclosure. Soft information determination circuitry 500 may consist offrequency metric accumulation circuitry 540 and reference valuedetermination circuitry 542. Frequency metric accumulation circuitry 540and reference value determination circuitry 542 may concurrently processbinary data 502 received as input. The output of frequency metricaccumulation circuitry 540 and the output of reference valuedetermination circuitry 542 may be merged by combiner 528 and providedas output signal 532. In some embodiments, frequency metric accumulationcircuitry 540 and reference value determination circuitry 542 may beused only when in retry mode, e.g., when a previous decoding attemptbased on hard information or soft information with lower granularity hasfailed. In particular, when retry mode is inactive, binary data 502 maynot be processed but simply passed through to multiplexer 504 whichoutputs binary data 502 as output signal 532 without further processing.In this case, no soft information may be generated. Alternatively, ifretry mode is active, processing by frequency metric accumulationcircuitry 540 and reference value determination circuitry 542 isperformed.

Frequency metric accumulation circuitry 540 includes comparator 510,multiplexers 512 and 524, adder 514, accumulation register 520,reference register 522, and bin index memory 526. Frequency metricaccumulation circuitry 540 may interface with buffer register 506 thattemporarily buffers binary data 502 for processing. For example, in someimplementations, buffer register 506 may have a width of 8 bits and maytherefore be able to accommodate 1 byte of data. Frequency metricaccumulation circuitry 540 may process data in buffer register 506 on aper bit basis, i.e., at a given time it may process a specific bitposition 508. After completing processing for specific bit position 508,it may process the next bit position until all bits in buffer register506 have been processed. Although not shown, other implementations, maybe considered without departing from the scope of the presentdisclosure, such as processing bits in buffer register 508 concurrentlyusing multiple hardware units each with separate frequency metricaccumulation circuitry 540.

Frequency metric accumulation circuitry 540 may process bit position 508of buffer register 506 by comparing the bit to a reference value usingcomparator 510. For example, in some implementations a reference valueof logical zero may be used. A value of logical one (not shown) may alsobe used as the reference value in some implementations. The outputsignal of comparator 510 is logical one if the bit stored in bitposition 508 is equal to the reference value; otherwise it is equal tological zero. Multiplexer 512 may receive the output signal ofcomparator 510 and output logical one if the output signal is equal tological one and may output logical zero otherwise. The output signal ofmultiplexer 512 may then be added to a previously accumulated value 523corresponding to the same bit position. Previously accumulated value 523may be stored in accumulation register 522. The result of the additionperformed by adder 514 may be stored in accumulation register 520 at thecorresponding bit position. In some implementations, accumulationregisters 520 and 522 may have a larger bit width than buffer register506, because accumulated value 515 after addition may have a value thatis greater than one and therefore cannot be expressed using a singlebit. For example, if accumulation value 515 can range from 0 to 15(e.g., corresponding to 15 read operations), then four bits may berequired in accumulation registers 520 and 522 for each bit position.Accordingly, in this example, the bit width of accumulation registers520 and 522 would be 32 bits. Of course, other bit widths may beconsidered without departing from the scope of the present disclosure.

Accumulation register 520 and register 522 may be connected to bin indexmemory 526. Registers 520 and 522 may be implemented to facilitate theread-modify-write function for bin index memory 526. Bin index memory526 may be used to store the accumulated frequency metric of binaryinput data 502. The width of bin index memory may therefore be equal tothe width of accumulation registers 520 and 522. The depth of the binindex memory 526 may depend on the size of a portion of binary inputdata 502 that is to be processed concurrently. For example, in someimplementations, the depth of bin index memory 526 may be designed suchthat a codeword can be processed concurrently. Accordingly, the depth ofbin index memory 526 may be determined by choosing it to be equal to thenumber of bytes in a codeword.

Frequency metric accumulation circuitry 540 may sequentially process thebit positions of buffer register 506 to obtain updated accumulatedvalues stored in accumulation register 520. Once the last bit positionin buffer register 506 has been processed, the values contained inaccumulation register 520 may be stored at a specific memory location inbin index memory 526. For example, a row in bin index memory 526 may beoverwritten with the values of accumulation register 520. Subsequently,binary data corresponding to a new read operation may be received bybuffer register 506 and processing may resume with the first bitposition of buffer register 506. When receiving binary input data 502associated with a new read operation of flash memory 102, previouslyaccumulated values may be retrieved from bin index memory 526 and storedin accumulation register 522. The values transferred into accumulationregister 522 are thus available for the previously-described addition byadder 514.

Once all read operations for a specific memory location have beencompleted, the frequency metric accumulated by frequency metricaccumulation circuitry 540 may be combined with a reference value bycombiner 528 and output as output signal 532. At the same time,accumulation register 522 may be reset by filling accumulation register522 with logical zeroes using zero sequence 526 and multiplexer 526.Multiplexer 524 may provide the zero sequence when a certain referencesignal is set. For example, this reference signal may be denoted“RTM_first_page.”

Reference value determination circuitry 542 receives binary data storedin buffer register 506 as input. As is described in relation to FIG. 3,the reference value reflects the value associated with a specific readoperation of flash memory 102 (or equivalently with a specific decisionthreshold 206 or 256). Accordingly, reference value determinationcircuitry 542 may not perform any processing unless the read operationcorresponding to the reference value is reached. In someimplementations, reference signal 530 may indicate whether the currentread operation corresponds to the reference value (this signal may alsobe denoted as “RTM_update_rmv”). If reference signal 530 is active, thebinary values of buffer register 506 are output and merged by combiner528 with the frequency metric signal 527. At the same time, referencesignal 530 may also trigger reference value memory 516 to store thebinary data contained in buffer register 506. Alternatively, ifreference signal 530 is inactive, no output signal may be provided andthe data contained in buffer memory 506 may not be processed byreference value determination circuitry 542.

Reference value output signal 534, determined by reference valuedetermination circuitry 540, and frequency metric output signal 536,determined by frequency metric accumulation circuitry 542, may be mergedby combiner 528. In some implementations, the combining performed bycombiner 528 may simply concatenate the bits contained in frequencymetric output signal 536 and reference value output signal 534. Forexample, if frequency metric output signal 536 uses 4 bits to expressthe accumulated frequency metric per bit position, and if one bit isused to express the reference value for each bit position, then aftercombining a total of 5 bits may be used per bit position. Accordingly,combiner 528 may simply increase the bit width of the output signal.Alternatively, combiner 528 may merge the information contained infrequency metric output signal 536 and reference value output signal 534by encoding it in a more compact form, for example by using some form ofjoint encoding. After combining, the output signal of combiner 528 maybe output by multiplexer 504 as output signal 532.

FIG. 6 shows an illustrative example of lookup table circuitry 600, inaccordance with some embodiments of the present disclosure. Lookup tablecircuitry 600 receives input signal 602 that includes a frequency metricand a reference value when retry mode is enabled. For example, inputsignal 602 may correspond to output signal 532 in FIG. 5. When retrymode is not enabled, frequency metric and reference value need not beincluded. Instead, as is described in relation to FIG. 5, output signalmay simply correspond to binary input sequence 502. Lookup tablecircuitry 600 may perform different types of processing depending on thetype of input signal 602, i.e., depending on whether frequency metricand reference value are included. If retry mode is disabled (andaccordingly frequency metric and reference value are not present), thebinary values of input signal 602 may simply be mapped to twolog-likelihood ratios, one corresponding to logical zero and one tological one. The mapping may be performed by HD-to-LLR mapper 602, whichmaps hard decisions (HDs) to LLRs. The output signal of HD-to-LLR mapper602 may be output by multiplexer 614 as output signal 616.

When retry mode is enabled, input signal 602 includes a frequency metricand a reference value for each input bit. For each bit, index remapper606 may convert the frequency metric and the reference value to a singleindex 607. For example, in some implementations the bits representingthe frequency metric and the bit representing the reference value maysimply be concatenated in a predefined order. The resulting bit sequencemay then be interpreted as an integer number to obtain index value 607.Alternatively, other types of mappings may be considered for associatingan index with the combination of frequency metric and reference value,without departing from the scope of the present disclosure. Index value607 is used to identity an element in at least one of LSB table 612, CSBtable 610, and MSB table 608. LSB table 612 may denote a tableassociated with the least significant bit of a symbol representingmultiple bits, CSB table 610 may denote a table associated with thecenter significant bit of a symbol representing multiple bits, and MSBtable may denote a table associated with the most significant bit of asymbol representing multiple bits. MSB table 608, CSB table 610, and LSBtable 612 may generate respective output signals that are received bymultiplexer 620. Multiplexer 620 may further receive a control signal618 that indicates which one of the output signals of MSB table 608, CSBtable 610, and LSB table 612 should be used. In some implementations,control signal 618 may also be referred to as “page_type.” The outputsignal of multiplexer 620 is received by multiplexer 614 and output asoutput signal 616 when retry mode is active. In some implementations,rather than generating a respective output signal for MSB table 608, CSBtable 610, and LSB table 612, only a single output signal for the lookuptable indicated by control signal 618 may be performed. This may savecomputational resources without impacting the output of lookup tablecircuitry 600, because control signal 618 anyway only selects a singleoutput signal of one of the lookup tables.

In some embodiments, bin index memory 526 and reference value memory 516may store the frequency metrics and the reference values for a number ofread operations. The stored frequency metrics and reference values maybe retrieved at predetermined times, and histograms may be generatedbased on the stored frequency metrics and reference values. In someembodiments histogram data may be stored in multiple histogram memoryunits. For example, eight histogram memory units may be included in softinformation generator 110, one such unit for each bit in buffer register506, i.e., for each bit in a byte of data. Histogram data may be storedin the histogram memory units by converting frequency metrics andreference values to a single index, for example by using index remapper606. The resulting index may be used to define a specific memorylocation. Every time a certain index occurs, the memory locationassociated with that index may be accessed and the stored valueretrieved. Next, this stored value may be incremented by one to accountfor the current occurrence of the index value, and the incremented valuemay be written back to the memory location. Accordingly, histogrammemory units may count the number of times that a certain bit locationin buffer register 506 is equal to a certain index value. In otherembodiments, instead of using separate histogram memory units for eachbit location in buffer register 506, a single histogram memory unit maybe used. If only a single histogram memory unit is available, it may notbe feasible to store histogram data for all bit locationssimultaneously, because the histogram memory unit may need to beaccessed approximately eight times more frequently compared to the casein which eight dedicated histogram memory units are used. In someimplementations, this issue may be avoided by storing only histogramdata associated with a subset of the bit locations in buffer register506. Alternatively, or additionally, histogram data may not be stored inreal-time but buffered in bin index memory 526 and reference valuememory 516. At predefined times, the data stored in these memories maybe retrieved and stored in a histogram memory unit. For example, thesepredefined times may correspond to time periods when the system operatesin non-retry mode and therefore need not generate soft information.

FIG. 7 shows an illustrative timing diagram 700 of decoding hardwareoperating in a non-retry mode, in accordance with an embodiment of thepresent disclosure. Timing diagram 700 may illustrate the operation ofsoft information generator 110 during “regular” operation, i.e., innon-retry mode. Timing diagram 700 includes mode indicator signal 702,page start indicator signal 704, page validity signal 706, and LLRoutput signal 708. Mode indicator signal 702 may indicate whether thesystem is currently operating in normal (i.e., non-retry mode) or inretry mode. Since timing diagram 700 illustrates the system's operationin normal mode, mode indicator signal 702 is equal to zero throughoutthe time period shown in FIG. 7. Page indicator signal 704 is a gatingsignal which denotes the beginning of a data segment or page by one ofpulses 704 a-704 d. For example, page indicator signal 704 indicates thebeginning of four respective pages over the time period shown in FIG. 7.Page validity signal 706 may indicate when read data is available forprocessing by soft information generator 110. In some implementations,there may be a short delay between a pulse of mode indicator signal 702and a pulse of page validity signal 706. This short delay may berequired in order to allow spurious emissions to settle or to accountfor some forms of processing delay. LLR output signal 708 may denotewhen output signals of soft information generator 110 are available. Forexample, LLR output signal 708 may be used by CPU 104 to scheduleprocessing tasks.

FIG. 8 shows an illustrative timing diagram for using decoding hardwareoperating in a retry mode, in accordance with some embodiments of thepresent disclosure. Timing diagram 800 may include mode indicator signal802, page start indicator signal 804, page validity signal 806, controlsignals 808, 810, and 812, as well as decoder status signal 814.

Mode indicator signal 802, page start indicator signal 804, and pagevalidity signal 806 may be similar to mode indicator signal 702, pagestart indicator signal 704 and page validity signal 706, as is discussedin relation to FIG. 7. However, in contrast to timing diagram 700,timing diagram 800 assumes that decoding using normal operation fails.For example, decoder status signal 814 may indicate using pulse 814 athat decoding using the regular operation mode has failed. In someimplementations, when decoder 112 fails to decode the signal in theregular operation mode, retry mode may initiated.

Initiating retry mode may require CPU 104 to change the scheduling ofcommands currently in a decoding pipeline. Accordingly, retry mode maynot be initiated immediately upon decoder status signal 814 indicatingthat decoding has failed. Instead, a pipeline break 801 may be needed inthe instructions pipeline, as illustrated in FIG. 8. After pipelinebreak 801, timing diagram 800 may be partitioned into time periods 820a-820 d (generally, time periods 820). During time periods 820 a-820 b,soft information generator 110 may be configured to generate softinformation based on two decision thresholds, one corresponding to afirst read operation during time period 820 a and one corresponding to asecond read operation 820 b. Specifically, during time period 820 a,control signal 808 indicates that new soft information needs to begenerated and therefore resets accumulation register 522, as isdiscussed in relation to FIG. 5. Control signal 808 remains inactiveduring the remainder of time periods 820 b-820 d because decoding of thesame data page continues to be processed. During the second readoperation in time period 820 b, control signal 812 is active, indicatingthat the reference value needs to be updated. At the end of time period820 b, soft information generator 110 has generated soft informationbased on the two read operations processed during time interval 820 aand 820 b. Accordingly, in time period 820 c, decoder 112 may performdecoding based on the LLR information. The processing of decoder 112 maybe indicated by decoder status signal 814. Timing diagram 800 assumesthat second decoding attempt 814 b during time period 820 c fails.Consequently, another decoding attempt using further refined softinformation may be attempted.

In order to further refine the soft information, a third and a fourthread operation may be performed during time interval 820 c and 820 d,respectively. Similar to time period 820 b, control signal 812 is activeduring time period 820 d in order to indicate that the reference valueshould be generated based on the binary values currently associated withthe input signal. During time period 820 e, the resulting LLRinformation is again received by decoder 112 which performs a thirddecoding attempt 814 c. Time diagram 800 assumes that the third decodingattempt, using the further refined soft information is successful.Although not shown in timing diagram 800, more than three decodingattempts may be performed by decoder 112, using even further refinedsoft information. The control signals shown in time diagram 800 aremeant to illustrate the processes that are performed by soft informationgenerator 110. However, additional control signals may be added or somecontrol signals may be removed or combined without departing from thescope of the present disclosure.

FIG. 9 shows a high-level flow diagram of a process 900 for determiningsoft information in a data storage system, in accordance with someembodiments of the present disclosure. Soft information generator 110may execute process 900 by, at 902, comparing a symbol value associatedwith a stored bit to a plurality of decision thresholds to obtain aplurality of binary values. Process 900 may, at 904, select, usingcontrol circuitry, one of the plurality of binary values to obtain areference value. At 906, process 900 may compute, using the controlcircuitry, a frequency metric corresponding to the number of times eachof the plurality of binary values equals a predefined value. Process 900may, at 908, determine the soft information metric based on thefrequency metric and the reference value.

At 902, a symbol value associated with a stored bit is compared to aplurality of decision thresholds to obtain a plurality of binary values.The symbol value may correspond to a modulation symbol that representsany number of bits, such as one bit (as shown in diagram 200) or twobits (as shown in diagram 250). The symbol value may be modulated usingany suitable type of modulation scheme, for example pulse-amplitudemodulation as illustrated in FIG. 2. The symbol value may be retrievedfrom flash device 102 using NAND interface 106 or using any othersuitable mechanism for accessing flash memory 102. The retrieved symbolvalue may be compared to a plurality of decision thresholds, as isdiscussed in relation to FIG. 2 and FIG. 3, and a plurality of binaryvalues may be obtained.

At 904, a reference value may be obtained by selecting one of theplurality of binary values corresponding to a predefined one of theplurality of decision thresholds. For example, the binary valuecorresponding to the rightmost decision threshold may be used, althoughany other decision threshold may be used alternatively without departingfrom the scope of the present disclosure. The reference value may beobtained using reference value determination circuitry 542. For example,the reference value may be identified based on a gating signal, such asgating signal 530 (“RTM_update_rmv”).

At 906, a frequency metric corresponding to the number of times theplurality of binary values equals a predefined value may be obtained. Insome implementations, the frequency metric may be obtained by frequencymetric accumulation circuitry 540. The frequency metric may be obtainedby counting the number of times that a reference value (e.g., eitherlogical zero or logical one) occurs in the bit sequences associated withthe decision thresholds. The number of occurrences of the referencevalue may be counted by buffering a binary input signal in bufferregister 506. For each bit location in the buffer register, the bitvalue may be compared to the reference value, and when both are equal,an accumulation register may be incremented. In some implementations,the content of accumulation registers may be stored in a bin indexmemory, for example to collect histogram data to be retrieved at a laterpoint in time.

At 908, a soft information metric may be determined based on thefrequency metric and the reference value. In some implementations, thesoft information metric may be determined by lookup table circuitry 404.For example, the frequency metric and the reference value obtained at904 and 906, respectively, may be converted (or “remapped”) to a singleindex. The resulting index value may be used to uniquely identify anelement in a lookup table. For example, the index value may identify aunique memory location in a stored lookup table and a soft informationmetric, stored at that index value may be retrieved. The softinformation metric may then be output by soft information generator 110and used by decoder 112 to decode the retrieved data.

In some embodiments, process 900 may be repeated several times, forexample until decoder 112 successfully decodes the data. In particular,soft information generator 110 may initially generate soft informationwith a low accuracy by using a first number of decision thresholds thatis relatively small. In some scenarios, the soft information metric thusgenerated may be insufficient for successful decoding by decoder 112. Ifdecoding is not successful, soft information generator 110 may generatemore accurate soft information by utilizing a second number of decisionthresholds, wherein the second number of decision thresholds is largerthan the first number of decision thresholds. This leads to moreaccurate soft information and decoder 112 may be able to decode theretrieved data based on the more accurate soft information. In someimplementations, a single decision threshold may be used in an initialdecoding attempt (e.g., in a non-retry mode), and only hard informationmay be computed in this initial decoding attempt. If initial decodingbased on hard information proves unsuccessful, soft information may begenerated and used in a second decoding attempt (e.g., in retry mode).

FIG. 10 is a block diagram 1000 of a computing device, such as any ofthe components of the system of FIG. 4, for performing any of theprocesses described herein, in accordance with some embodiments of thepresent disclosure. Each of the components of these systems may beimplemented on one or more computing devices 1000. In certain aspects, aplurality of the components of these systems may be included within onecomputing device 1000. In certain embodiments, a component and a storagedevice 1011 may be implemented across several computing devices 1000.

The computing device 1000 comprises at least one communicationsinterface unit 1008, an input/output controller 1010, system memory1003, and one or more data storage devices 1011. The system memory 1003includes at least one random access memory (RAM 1002) and at least oneread-only memory (ROM 1004). All of these elements are in communicationwith a central processing unit (CPU 1006) to facilitate the operation ofthe computing device 1000. The computing device 1000 may be configuredin many different ways. For example, the computing device 1000 may be aconventional standalone computer or alternatively, the functions ofcomputing device 1000 may be distributed across multiple computersystems and architectures. In FIG. 10, the computing device 1000 islinked, via network 1018 or local network, to other servers or systems.

The computing device 1000 may be configured in a distributedarchitecture, wherein databases and processors are housed in separateunits or locations. Some units perform primary processing functions andcontain at a minimum a general controller or a processor and a systemmemory 1003. In distributed architecture embodiments, each of theseunits may be attached via the communications interface unit 1008 to acommunications hub or port (not shown) that serves as a primarycommunication link with other servers, client or user computers andother related devices. The communications hub or port may have minimalprocessing capability itself, serving primarily as a communicationsrouter. A variety of communications protocols may be part of the system,including, but not limited to: Ethernet, SAP, SAS™, ATP, BLUETOOTH™, GSMand TCP/IP.

The CPU 1006 comprises a processor, such as one or more conventionalmicroprocessors and one or more supplementary co-processors such as mathco-processors for offloading workload from the CPU 1006. The CPU 1006 isin communication with the communications interface unit 1008 and theinput/output controller 1010, through which the CPU 1006 communicateswith other devices such as other servers, user terminals, or devices.The communications interface unit 1008 and the input/output controller1010 may include multiple communication channels for simultaneouscommunication with, for example, other processors, servers or clientterminals.

The CPU 1006 is also in communication with the data storage device 1011.The data storage device 1011 may comprise an appropriate combination ofmagnetic, optical or semiconductor memory, and may include, for example,RAM 1002, ROM 1004, flash drive, an optical disc such as a compact discor a hard disk or drive. The CPU 1006 and the data storage device 1011each may be, for example, located entirely within a single computer orother computing device; or connected to each other by a communicationmedium, such as a USB port, serial port cable, a coaxial cable, anEthernet cable, a telephone line, a radio frequency transceiver or othersimilar wireless or wired medium or combination of the foregoing. Forexample, the CPU 1006 may be connected to the data storage device 1011via the communications interface unit 1008. The CPU 1006 may beconfigured to perform one or more particular processing functions.

The data storage device 1011 may store, for example, (i) an operatingsystem 1012 for the computing device 1000; (ii) one or more applications1014 (e.g., computer program code or a computer program product) adaptedto direct the CPU 1006 in accordance with the systems and methodsdescribed here, and particularly in accordance with the processesdescribed in detail with regard to the CPU 1006; or (iii) database(s)1016 adapted to store information that may be utilized to storeinformation required by the program.

The operating system 1012 and applications 1014 may be stored, forexample, in a compressed, an uncompiled and an encrypted format, and mayinclude computer program code. The instructions of the program may beread into a main memory of the processor from a computer-readable mediumother than the data storage device 1011, such as from the ROM 1004 orfrom the RAM 1002. While execution of sequences of instructions in theprogram causes the CPU 1006 to perform the process steps describedherein, hard-wired circuitry may be used in place of, or in combinationwith, software instructions for embodiment of the processes of thepresent disclosure. Thus, the systems and methods described are notlimited to any specific combination of hardware and software.

Suitable computer program code may be provided for performing one ormore functions in relation to soft information generation as describedherein. The program also may include program elements such as anoperating system 1012, a database management system and “device drivers”that allow the processor to interface with computer peripheral devices(e.g., a video display, a keyboard, a computer mouse, etc.) via theinput/output controller 1010.

The term “computer-readable medium” as used herein refers to anynon-transitory medium that provides or participates in providinginstructions to the processor of the computing device 1000 (or any otherprocessor of a device described herein) for execution. Such a medium maytake many forms, including, but not limited to, non-volatile media andvolatile media. Non-volatile media include, for example, optical,magnetic, or opto-magnetic disks, or integrated circuit memory, such asflash memory. Volatile media include dynamic random access memory(DRAM), which typically constitutes the main memory. Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM,DVD, any other optical medium, punch cards, paper tape, any otherphysical medium with patterns of holes, a RAM, a PROM, an EPROM orEEPROM (electronically erasable programmable read-only memory), aFLASH-EEPROM, any other memory chip or cartridge, or any othernon-transitory medium from which a computer may read.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to the CPU 1006 (or anyother processor of a device described herein) for execution. Forexample, the instructions may initially be borne on a magnetic disk of aremote computer (not shown). The remote computer may load theinstructions into its dynamic memory and send the instructions over anEthernet connection, cable line, or even telephone line using a modem. Acommunications device local to a computing device 1000 (e.g., a server)may receive the data on the respective communications line and place thedata on a system bus for the processor. The system bus carries the datato main memory, from which the processor retrieves and executes theinstructions. The instructions received by main memory may optionally bestored in memory either before or after execution by the processor. Inaddition, instructions may be received via a communication port aselectrical, electromagnetic or optical signals, which are exemplaryforms of wireless communications or data streams that carry varioustypes of information.

While various embodiments of the present disclosure have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the disclosure. It should beunderstood that various alternatives to the embodiments of thedisclosure described herein may be employed in practicing thedisclosure. It is intended that the following claims define the scope ofthe disclosure and that methods and structures within the scope of theseclaims and their equivalents be covered thereby.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications can be made without departing fromthe scope of the present disclosure. The above described embodiments ofthe present disclosure are presented for purposes of illustration andnot of limitation, and the present disclosure is limited only be theclaims which follow.

What is claimed is:
 1. A method for decoding a stored bit, comprising:generating, using a demodulator, a plurality of binary values bycomparing a symbol value of a stored bit to a plurality of thresholds,the plurality of binary values corresponding to a decision region boundby one or more of the plurality of thresholds; computing, using controlcircuitry and the plurality of binary values, a frequency metric;selecting, using the control circuitry, one of the plurality of binaryvalues as a reference value; and determining, using the controlcircuitry, a soft information metric based on the frequency metric andthe reference value.
 2. The method of claim 1, wherein the frequencymetric corresponds to a number of times a predefined value occurs in theplurality of binary values.
 3. The method of claim 1, wherein thereference value corresponds to one of the plurality of binary valuesthat corresponds to a reference threshold of the plurality ofthresholds.
 4. The method of claim 1, further comprising: performing adecoding attempt based on hard information; and in response todetermining that the decoding attempt has failed, generating theplurality of binary values by comparing the symbol value to theplurality of thresholds.
 5. The method of claim 1, wherein the softinformation metric is a first soft information metric having a firstgranularity, the method further comprising: performing a decodingattempt based on the first soft information metric; and in response todetermining that the decoding attempt has failed, generating a secondsoft information metric having a second granularity, wherein the secondgranularity is finer than the first granularity.
 6. The method of claim1, wherein the soft information metric is a log-likelihood ratio (LLR)metric.
 7. The method of claim 1, wherein determining the softinformation metric comprises: identifying an index value based on thefrequency metric and the reference value; and determining the softinformation metric from a lookup table based on the index value.
 8. Themethod of claim 1, wherein the number of thresholds in the plurality ofthresholds is determined based on a desired granularity of the softinformation metric.
 9. The method of claim 1, wherein the plurality ofthresholds are adapted based on statistics associated with the softinformation metric.
 10. The method of claim 1, further comprising:generating an index value based on the frequency metric and thereference value; and storing a histogram of the index value to obtainstatistics of the soft information metric.
 11. A system for decoding astored bit, comprising: a demodulator configured to generate a pluralityof binary values by comparing a symbol value of a stored bit to aplurality of thresholds, wherein the plurality of binary valuescorresponds to a decision region bound by one or more of the pluralityof thresholds; and control circuitry configured to: compute, using theplurality of binary values, a frequency metric, select one of theplurality of binary values as a reference value, and determine a softinformation metric based on the frequency metric and the referencevalue.
 12. The system of claim 11, wherein the frequency metriccorresponds to a number of times a predefined value occurs in theplurality of binary values.
 13. The system of claim 11, wherein thereference value corresponds to one of the plurality of binary valuesthat corresponds to a reference threshold of the plurality ofthresholds.
 14. The system of claim 11, wherein the control circuitry isfurther configured to: perform a decoding attempt based on hardinformation, and in response to determining that the decoding attempthas failed, generate the plurality of binary values by comparing thesymbol value to the plurality of thresholds.
 15. The system of claim 11,wherein the soft information metric is a first soft information metrichaving a first granularity, and the control circuitry is furtherconfigured to: perform a decoding attempt based on the first softinformation metric, and in response to determining that the decodingattempt has failed, generate a second soft information metric having asecond granularity, wherein the second granularity is finer than thefirst granularity.
 16. The system of claim 11, wherein the softinformation metric is a log-likelihood ratio (LLR) metric.
 17. Thesystem of claim 11, wherein the control circuitry is further configuredto: identify an index value based on the frequency metric and thereference value, and determine the soft information metric from a lookuptable based on the index value.
 18. The system of claim 11, wherein thenumber of thresholds in the plurality of thresholds is determined basedon a desired granularity of the soft information metric.
 19. The systemof claim 11, wherein the plurality of thresholds are adapted based onstatistics associated with the soft information metric.
 20. The systemof claim 11, wherein the control circuitry is further configured to:generate an index value based on the frequency metric and the referencevalue, and store a histogram of the index value to obtain statistics ofthe soft information metric.